{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from dynamic_panel_dgp import DynamicPanelDGP\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy \n",
    "from coverage_panel import add_vlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import joblib\n",
    "n_exps = 10\n",
    "dirname = os.path.join('results', 'constant')\n",
    "param_str = (\"n_exps_{}_n_units_500_n_periods_3_n_t_2_n_x_450_s_x_2\"\n",
    "              \"_s_t_2_sigma_x_1_sigma_t_1_sigma_y_1_conf_str_1_gamma_0.2\").format(n_exps)\n",
    "results = joblib.load(os.path.join(dirname, \"results_{}.jbl\".format(param_str)))\n",
    "dgp = joblib.load(os.path.join(dirname, \"dgp_obj_{}.jbl\".format(param_str)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_periods = dgp.n_periods\n",
    "n_treatments = dgp.n_treatments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "results = np.array(results)\n",
    "points = results[:, 0]\n",
    "lowers = results[:, 1]\n",
    "uppers = results[:, 2]\n",
    "stderrs = results[:, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wo48CgHf0UkSY5wDDWtsyxvyyuquLxCX9nrX2vDHml7Z//sB5L4BIKBa7t7lcsHUAQFjR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\nAAAAAMB5/z+N90I4j7TBvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_effect_params = dgp.true_effect.flatten()\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "inds = np.arange(points.shape[1])\n",
    "plt.violinplot(points, positions=inds, showmeans=True)\n",
    "plt.scatter(inds, true_effect_params, marker='o',\n",
    "            color='#D43F3A', s=10, zorder=3, alpha=.5)\n",
    "add_vlines(n_periods, n_treatments)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "inds = np.arange(points.shape[1])\n",
    "plt.violinplot(stderrs, positions=inds, showmeans=True)\n",
    "true_std = np.std(points, axis=0)\n",
    "true_std_error = (true_std * (np.sqrt((n_exps-1)/scipy.stats.chi2.ppf((1-.05/2), n_exps-1)) - 1),\n",
    "                  true_std * (1 - np.sqrt((n_exps-1)/scipy.stats.chi2.ppf((.05/2), n_exps-1))))\n",
    "plt.errorbar(inds, true_std, yerr=true_std_error, fmt='o',\n",
    "            color='#D43F3A', elinewidth=2, alpha=.9, capthick=.5, uplims=True, lolims=True)\n",
    "add_vlines(n_periods, n_treatments)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L1h324SSPttbeleQnk/xWVZ2yZv9PtdYuba3tWMzYsOpv/3a2ABg3eQ4wHTKdjsxzBcbl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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coverage = np.mean((true_effect_params.reshape(1, -1) <= uppers) & (\n",
    "    true_effect_params.reshape(1, -1) >= lowers), axis=0)\n",
    "plt.figure(figsize=(15, 5))\n",
    "inds = np.arange(points.shape[1])\n",
    "plt.scatter(inds, coverage)\n",
    "add_vlines(n_periods, n_treatments)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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